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2022
Master Thesis
Title

Multi View Focus Stacking

Title Supplement
Via Graph Cut and Neural Radiance Fields
Abstract
Focus stacking is a frequently used technique in photography [6]. Also, this technique can be applied in product photography to extend the depth of field in the images. In product photography, the object is captured from multiple views. In my approach to focus stacking, I want to use this additional information from multiple views to improve the focus stacking of every individual view. The method consists of three steps. First, the image is stacked with an initial stacking that only uses pixel information. Second, this initial information is used with Graph Cut [2] to improve the sharpness selection. In the third step, all images are combined with NeuralRadiance Fields (NeRF) [10]. The evaluation shows that Graph Cut improves the sharpness detection compared to the initial detection. And I show that NeRF is not a suitable model for focus stacking because it fails when the images have artefacts generated by the focus stacking.
Thesis Note
Darmstadt, TU, Master Thesis, 2022
Author(s)
Wempe, Leon Julia
Advisor(s)
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Tausch, Reimar  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Domajnko, Matevz  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Digitized Work

  • Lead Topic: Visual Computing as a Service

  • Research Line: Computer graphics (CG)

  • Research Line: Computer vision (CV)

  • Camera calibration

  • Multi image stacks

  • Digital images

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